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I’d like to respond to two issues you raise:
– the skill of predicting snow melt in spring summer. As I also explained in previous post (just above this one), the models do this fairly well primarily because we know the amount of snow present in a basin at the start of a forecast also fairly well. So if you mean by ‘linking forecast’s to present large scale weather’ linking it to present large scale snow distribution, then that is exactly what the forecast models do!
– however if you mean linking it to the present atmospheric state, that is not something we do (yet), but that might be a good idea to explore and do in the future. Then we would be issueing so-called conditional forecasts. For example, if in December we are in a specific strong positive or negative state of the North Atlantic Oscillation (NAO, an important predictor of European weather), then we might have more confidence in our forecasts than if the NAO is weakly developed.
To some extend this is done implicitly in our model-chain as our maps show high, medium, more low probabilities of e.g. Above Normal rain or river flow. A high probability means we are more confident about the forecast. However, at this stage we cannot tell you why (in causal terms) it is high, we only know then that the majority of the model runs predict this. More research (and operational development) is needed before we can tell you for each forecast why high or low river flows are predicted.
I am happy to hear the river flow forecasts performed well so far. You have to realize that the influence of snow accumulation in winter, and snow melting in spring/summer, is explicitly accounted for in our forecast. In fact it is a very important source of the skill we see. Based on precipitation and temperature (corrected for altitude, see below) the model continuously computes the thickness of the snowpack (in so-called snow-water equivalents). Based on that we have a fairly good idea how much snow there is in any basin at the start of a forecast. This so-called inital condition provides so much ‘memory’ to the system that it allows the model to predict snow melting and therefore river flow in spring/early summer fairly well. In fact the (poor) quality of the precipitation forecast becomes less important, especially when -like in your basin- in the melting season the rainfall is relatively low.
Further you mention that temperatures are overestimated. I dont know compared to what reference, but of course temperature is very sensitive to altitude. So if you compare predicted temperatures to some mountain weather station data, there will generally be a systematic bias. A bias that is fairly constant and that can easily be corrected, if we know the difference between station altitude and model terrain elevation. Having said that temperature anomaly forecasts are generally much better than precipitation anomaly forecasts, I would expect so too in you region. Note that I say ‘anomalies’: they are by definition not senstitive to systematic errors!
Finally, to connect what I said in the previous two paragraphs: for snow accumulation and melt our models work with altitude bands. So even if your basin is represented by only a few model units (sub basins in the EHYPE, grid cells in may other models), within each unit the models keep track of different altitudes with each a different temperature to account for the unresolved topography of the mountains.
Good to hear that you managed (with the help of the Demonstrator) to explain to your clients the nature of the uncertainty in climate change projections. And that together you analysed the robust messages from the data cloud rather than the uncertainties.
This once more confirms the fact that climate services, in order to be successful, require close and sustained collaboration between purveyor and client.
If, based on your interactions with the client vis-a-vis the Demonstreator, you still have concerns on things that were are hard to explain or if you have tips for us to improve the Demonstrator we’d like to hear!
Dear Sara, Louise,
indeed skill in forecasting streamflow is varies (a lot) with lead time, time of the year and river basin. And the forecast quality for discharge can be better than that for precipitation. For Spain we know from previous work (NOT this EHYPE model, but statistical models) that streamflow forecast quality is quite good for at least Central, Western Spain (Douro, Tejo, Guadiana) for winter and early spring (DJFM). Also our own model shows potential skill in this period and basins, but also in july/august more towards your area (Mediterranean basins).
Another issue is the used metric for the skill. The maps presented are masked out based on CPRS, this is a measure across all flow percentiles and therefore perhaps a bit strict. Other skill scores that measure the performance of forecasting only for high flow or low flows, such as ROCS may give less conservative indications of forecast quality. Refering the previous examples: in winter high flows are better predicted than low flows, in summer the other way around: low flows are better predicted than high flows. Moreover, more extreme anomalies are often better forecasted than less extreme ones.
May be Louise can comment a bit more already on winter skill and skill for high or low flows for the EHYPE model.